Following the idea of purely expanding the map into unknown regions, frontier-based exploration [1]} has been widely adopted. Real-world demonstrations in [2]}, [3]} show its applicability in both 2-D and 3-D environments. To achieve high energy efficiency during exploration, [4]} proposes to select the frontier within the agent's FOV to maintain a high flight speed. Instead of searching frontiers, maximizing the information gain serves as another heuristic to guide exploration. nbv is sampled from rrt [5]}, [6]} or motion primitives [7]} to maximize information gain. Different methods are also proposed to evaluate the information gain [8]}, [9]}, [10]}. However, classical methods assume all voxels in the ray-casting process will be updated, resulting in an overestimate of total information gain. Hierarchical approaches have inherent advantages by benefiting from multiple strategies. In [11]}, [12]}, global paths are planned towards frontiers while local paths are sampled through motion primitives and optimized for information gain. [13]}, [14]} plans a global coverage path through tsp to efficiently travel through all viewpoints and sample viewpoints around frontiers to maximize coverage or information gain. To further improve the efficiency and consistency of exploration, semantic and topological information is used in [15]}, [16]} to assist high-level robot behaviors. However, directly incorporating the semantic information into the utility function sometimes fails to guide the exploration, as the semantic information might be labelled incorrectly. In [17]}, nbv is selected following a decision model to guide the exploration behavior. However, it assumes that a semantic mapping model will provide perfect semantic information of the environment.
